CN111966453A - Load balancing method, system, equipment and storage medium - Google Patents
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- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
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- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
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Abstract
The invention discloses a load balancing method, a system, equipment and a storage medium, wherein the method comprises the following steps: monitoring the node resource utilization rate of the cluster; when the resource utilization rate of the nodes exceeds a preset early warning threshold value, simulating the same service deployment for each node, predicting and calculating the resource utilization rate quantitative scores of different nodes, and selecting increased and decreased cluster nodes according to the quantitative scores; simulating the deployment and distribution of cluster services, predicting and calculating balance scores of different services deployed on each node, and adjusting the service distribution of the cluster nodes according to the balance scores. The system comprises a node state monitoring module, a node number configuration module and a node service configuration module. The invention carries out business classification modeling prediction and mixed business resource prediction on the resource usage of the container, thereby improving the node resource utilization rate and the node benefit.
Description
Technical Field
The invention relates to the field of server virtualization, in particular to a load balancing method, system, equipment and storage medium.
Background
The server virtualization solution can convert a static and complex IT environment into a more dynamic and easily-managed virtual data center through fusion, distribution and management of underlying physical resources, improves the agility and flexibility of resource delivery and the use efficiency of resources, helps enterprises to create a high-performance, extensible, manageable and flexible server virtualization infrastructure, and provides high-quality virtual data center services.
The server virtualization solution is based on an open-source system virtualization module KVM (Kernel-based Virtual Machine) design, and can improve the reliability, availability and safety of a virtualization system. The KVM may be used as a part of a Linux kernel to run multiple virtual machines on a set of physical hardware securely, but since the virtual machines provided by the virtualization technology use independent operating systems, the resource occupation and consumption of the operating systems have the problems of resource waste and starting speed.
The application of container technology greatly improves the resource utilization rate of physical resources, provides faster response time and can bear larger concurrent access amount. The appearance of the container arrangement tool increases the parallel processing capacity, accelerates the request response speed, and solves the problems of low resource utilization rate of the cloud platform, slow scheduling and distribution and the like. In this premise background, task scheduling becomes one of the key issues that the data center needs to solve.
The load balancing scheduling algorithm of the container arrangement tool can effectively distribute container cluster resources, improve the resource utilization rate and minimize the total cost of resource consumption, but the problem of uneven load can occur after the system is found to operate for a long time in the operation and maintenance process, the resource distribution among different hosts can have great difference, and the phenomena of overlarge consumption pressure of part of nodes, unbalanced resource distribution and the like can occur.
Disclosure of Invention
In order to solve the technical problems, the invention provides a load balancing method, a system, equipment and a storage medium, which are used for carrying out business classification modeling prediction and mixed business resource prediction on the resource usage of a container, and improving the node resource utilization rate and the node benefit.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of load balancing, comprising:
monitoring the node resource utilization rate of the cluster;
when the resource utilization rate of the nodes exceeds a preset early warning threshold value, simulating the same service deployment for each node, predicting and calculating the resource utilization rate quantitative scores of different nodes, and selecting increased and decreased cluster nodes according to the quantitative scores;
simulating the deployment and distribution of cluster services, predicting and calculating balance scores of different services deployed on each node, and adjusting the service distribution of the cluster nodes according to the balance scores.
Further, when the node resource utilization rate exceeds a preset early warning threshold, the same service deployment is simulated for each node, the resource utilization rate quantitative scores of different nodes are calculated in a predictive manner, and the cluster nodes which are increased or decreased are selected according to the quantitative scores and specifically are as follows:
when the node resource utilization rate is higher than a preset high early warning threshold value, selecting an available node according to a preset screening condition;
simulating service deployment for the selected available nodes, setting weights for different resource utilization rates of the nodes, calculating weighted utilization rate quantitative scores of all resources of each node in an acquisition period, and selecting the node with the highest score to add to the cluster nodes;
when the node resource utilization rate is lower than a preset low early warning threshold value, simulating service deployment for cluster nodes, setting weights for different resource utilization rates of the nodes, calculating weighted utilization rate quantitative scores of all resources of each node in an acquisition period, and closing the cluster nodes with the lowest scores.
Further, the resources include one or more of CPU resources, memory resources, I/O resources, and disk resources.
Further, the simulating the cluster service deployment allocation, predicting and calculating the balance score of different services deployed on each node, and adjusting the cluster node service allocation according to the balance score specifically includes:
simulating and calculating the average consumption values of different resources of the sample nodes, and obtaining the ratio of the average consumption value of each resource to the node consumption value as the resource consumption coefficient;
and calculating the ratio of the consumption coefficients of each resource of the node under two different service collocations, taking the sum of the consumption coefficient ratios as the balance score of the node, and selecting a group of service collocations with the maximum balance score as the services distributed by the node.
Further, the resources include one or more of CPU resources, memory resources, I/O resources, and disk resources.
The invention also provides a load balancing system, comprising:
the node state monitoring module is used for monitoring the node resource utilization rate of the cluster;
the node quantity configuration module is used for predicting and calculating the resource utilization rate quantitative scores of different nodes by simulating the same service deployment for each node when the node resource utilization rate exceeds a preset early warning threshold value, and selecting increased and decreased cluster nodes according to the quantitative scores;
and the node service configuration module is used for simulating the deployment and distribution of the cluster services, predicting and calculating the balance scores of different services deployed on each node, and adjusting the distribution of the cluster node services according to the balance scores.
The invention also provides a load balancing device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the load balancing method as described above when executing the computer program.
The invention also proposes a storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the load balancing method as described above.
The invention has the beneficial effects that:
the invention optimizes the scheduling strategy of the host machine by providing a load balancing method, a system, equipment and a storage medium, realizes the balancing and high-efficiency response of cluster loads by predicting the loads, performs service classification modeling prediction and mixed service resource prediction on the resource usage of the container, and improves the node resource utilization rate and the node benefit.
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FIG. 1 is a flow chart of a load balancing method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a load balancing system according to an embodiment of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
At present, load balancing algorithms represented by Kubernetes and Swarm are arranged in containers and node preselection is carried out according to the current resource situation and the application amount of container resources, only consumption of a CPU and a memory is considered during load balancing algorithm scheduling, and disk occupancy rate and I/O performance are not considered, so that phenomena of node distribution imbalance, performance parameter imbalance, large resource consumption difference and the like can occur after the load balancing algorithms are operated for a period of time.
As shown in fig. 1, an embodiment of the present invention discloses a load balancing method, including:
monitoring the node resource utilization rate of the cluster;
when the resource utilization rate of the nodes exceeds a preset early warning threshold value, simulating the same service deployment for each node, predicting and calculating the resource utilization rate quantitative scores of different nodes, and selecting increased and decreased cluster nodes according to the quantitative scores;
simulating the deployment and distribution of cluster services, predicting and calculating balance scores of different services deployed on each node, and adjusting the service distribution of the cluster nodes according to the balance scores.
Specifically, when the node resource utilization rate exceeds a preset early warning threshold, the same service deployment is simulated for each node, the resource utilization rate quantitative scores of different nodes are calculated in a predictive manner, and the cluster nodes which are increased or decreased are selected according to the quantitative scores and specifically are as follows:
when the node resource utilization rate is higher than a preset high early warning threshold value, selecting an available node according to a preset screening condition;
simulating service deployment for the selected available nodes, setting weights for different resource utilization rates of the nodes, calculating weighted utilization rate quantitative scores of all resources of each node in an acquisition period, and selecting the node with the highest score to add to the cluster nodes;
when the node resource utilization rate is lower than a preset low early warning threshold value, simulating service deployment for cluster nodes, setting weights for different resource utilization rates of the nodes, calculating weighted utilization rate quantitative scores of all resources of each node in an acquisition period, and closing the cluster nodes with the lowest scores.
Selecting available nodes according to preset screening conditions, and excluding nodes meeting one of the following conditions:
the node is in abnormal operation state;
the CPU, the memory, the disk or the I/O exceeds a preset alarm threshold;
and the node residual resources do not meet the pre-application resources.
And the high early warning threshold value and the low early warning threshold value are planned and set according to the overall resource and node service requirements of the cluster.
And for the condition that the node resource utilization rate is higher than a preset high early warning threshold value, simulating available nodes with the same service deployment and real environment resource configuration to perform mixed service batch deployment. And setting weight for the actual resource use condition of an available node. The resource load condition of each candidate node in the cluster is monitored in real time, the resource load condition in a period is collected, and the quantitative score can be expressed as follows in a formula form:
wherein x, y and z respectively represent the weight of a CPU resource, a memory resource and an I/O resource, and satisfy that x + y + z is 1; within an acquisition cycle of each resource utilization there are n acquisition points, SiCpnRepresenting CPU usage, S, of the ith acquisition PointimemRepresenting the memory usage of the ith acquisition Point, Sii/oRepresenting the I/O usage of the ith collection point. And the node with the highest Score of the Score1 is used for optimally selecting a deployment node and adding the deployment node to the cluster node.
And for the condition that the node resource utilization rate is lower than the preset low early warning threshold, adopting a simulation method similar to the method to perform simulated service deployment on the cluster nodes, calculating the actual resource use condition setting weight of a certain cluster node, and setting the node with the lowest Score of the Score1 as a closed redundant node.
Such resources include, but are not limited to, CPU resources, memory resources, I/O resources, or disk resources.
For the cluster with the redetermined node number, service deployment needs to be redistributed, the balance score for deploying different services on each node is calculated by simulating cluster service deployment and distribution, and the adjustment of cluster node service distribution according to the balance score specifically comprises the following steps:
performing service simulation through a Selenium automatic tool, calculating average consumption values of different resources of a sample node, and obtaining a ratio of the average consumption value of each resource to the node consumption value as a resource consumption coefficient;
and calculating the ratio of the consumption coefficients of each resource of the node under two different service collocations, taking the sum of the consumption coefficient ratios as the balance score of the node, and selecting a group of service collocations with the maximum balance score as the services distributed by the node. The simulation calculation process uses the following formula:
CCPU/ACPV＝α
Cmem/Amem＝β
Ci/0/Ai/o＝μ
wherein, CCPURepresenting the average CPU resource consumption, n being the number of sample nodes, ACPURepresenting CPU resource node consumption value, CmemRepresents the average consumption value of memory resources, AmemRepresenting the consumption value of the memory resource node, Ci/0Denotes the average I/O resource consumption value, Ai/oRepresenting the I/O resource node consumption value, CstorRepresenting the average consumption value of disk resources, AstorRepresenting a disk resource node consumption value; alpha represents the ratio of the average consumption value of the CPU resource to the consumption value of the node, beta represents the ratio of the average consumption value of the memory resource to the consumption value of the node, mu represents the ratio of the average consumption value of the I/O resource to the consumption value of the node,
the ratio of the average consumption value of the disk resources to the consumption value of the node is represented, and i and j represent two different service types. The larger the ratio of alpha i/alpha j is, the more suitable the i and j service combination is placed in one node, and similarly, the larger the ratio of beta i/beta j is, the more suitable the i and j services are placed in the same node. Score2 is the balance Score of the node, and the group of service types i and j with the maximum balance Score is the most suitable for the nodeLoad balancing business scheme.
Such resources include, but are not limited to, CPU resources, memory resources, I/O resources, or disk resources.
As shown in fig. 2, an embodiment of the present invention further provides a load balancing system, including:
the node state monitoring module is used for monitoring the node resource utilization rate of the cluster;
the node quantity configuration module is used for predicting and calculating the resource utilization rate quantitative scores of different nodes by simulating the same service deployment for each node when the node resource utilization rate exceeds a preset early warning threshold value, and selecting increased and decreased cluster nodes according to the quantitative scores;
and the node service configuration module is used for simulating the deployment and distribution of the cluster services, predicting and calculating the balance scores of different services deployed on each node, and adjusting the distribution of the cluster node services according to the balance scores.
An embodiment of the present invention further provides a load balancing device, including:
a memory for storing a computer program;
a processor for implementing the steps of the load balancing method as described above when executing the computer program.
The embodiment of the present invention further provides a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the load balancing method are implemented.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the scope of the present invention is not limited thereto. Various modifications and alterations will occur to those skilled in the art based on the foregoing description. And are neither required nor exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or changes which can be made by a person skilled in the art without creative efforts are still within the protection scope of the invention.
Claims (8)
1. A method of load balancing, comprising:
monitoring the node resource utilization rate of the cluster;
when the resource utilization rate of the nodes exceeds a preset early warning threshold value, simulating the same service deployment for each node, predicting and calculating the resource utilization rate quantitative scores of different nodes, and selecting increased and decreased cluster nodes according to the quantitative scores;
simulating the deployment and distribution of cluster services, predicting and calculating balance scores of different services deployed on each node, and adjusting the service distribution of the cluster nodes according to the balance scores.
2. The load balancing method according to claim 1, wherein when the node resource utilization rate exceeds a preset early warning threshold, the same service deployment is simulated for each node, and quantitative scores of the resource utilization rates of different nodes are calculated in a predictive manner, and the selecting of the increased and decreased cluster nodes according to the quantitative scores specifically includes:
when the node resource utilization rate is higher than a preset high early warning threshold value, selecting an available node according to a preset screening condition;
simulating service deployment for the selected available nodes, setting weights for different resource utilization rates of the nodes, calculating weighted utilization rate quantitative scores of all resources of each node in an acquisition period, and selecting the node with the highest score to add to the cluster nodes;
when the node resource utilization rate is lower than a preset low early warning threshold value, simulating service deployment for cluster nodes, setting weights for different resource utilization rates of the nodes, calculating weighted utilization rate quantitative scores of all resources of each node in an acquisition period, and closing the cluster nodes with the lowest scores.
3. The load balancing method of claim 2, wherein the resources include one or more of CPU resources, memory resources, I/O resources, and disk resources.
4. The load balancing method according to claim 1, wherein the simulating of the cluster service deployment allocation predicts and calculates a balancing score for deploying different services on each node, and the adjusting of the cluster node service allocation according to the balancing score specifically comprises:
simulating and calculating the average consumption values of different resources of the sample nodes, and obtaining the ratio of the average consumption value of each resource to the node consumption value as the resource consumption coefficient;
and calculating the ratio of the consumption coefficients of each resource of the node under two different service collocations, taking the sum of the consumption coefficient ratios as the balance score of the node, and selecting a group of service collocations with the maximum balance score as the services distributed by the node.
5. The load balancing method of claim 4, wherein the resources comprise one or more of CPU resources, memory resources, I/O resources, and disk resources.
6. A load balancing system, comprising:
the node state monitoring module is used for monitoring the node resource utilization rate of the cluster;
the node quantity configuration module is used for predicting and calculating the resource utilization rate quantitative scores of different nodes by simulating the same service deployment for each node when the node resource utilization rate exceeds a preset early warning threshold value, and selecting increased and decreased cluster nodes according to the quantitative scores;
and the node service configuration module is used for simulating the deployment and distribution of the cluster services, predicting and calculating the balance scores of different services deployed on each node, and adjusting the distribution of the cluster node services according to the balance scores.
7. A load balancing device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the load balancing method according to any one of claims 1 to 5 when executing the computer program.
8. A storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the load balancing method according to any one of claims 1 to 5.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112650363A (en) * | 2020-12-11 | 2021-04-13 | 北京四方继保工程技术有限公司 | Distributed telemechanical system based on balanced communication CPU node group and design method |
| CN113568746A (en) * | 2021-07-27 | 2021-10-29 | 北京达佳互联信息技术有限公司 | Load balancing method and device, electronic equipment and storage medium |
| CN113626282A (en) * | 2021-07-16 | 2021-11-09 | 济南浪潮数据技术有限公司 | Cloud computing physical node load monitoring method and device, terminal and storage medium |
| CN113656046A (en) * | 2021-08-31 | 2021-11-16 | 北京京东乾石科技有限公司 | Application deployment method and device |
| CN114466019A (en) * | 2022-04-11 | 2022-05-10 | 阿里巴巴（中国）有限公司 | Distributed computing system, load balancing method, device and storage medium |
| CN114675956A (en) * | 2022-04-14 | 2022-06-28 | 三峡智控科技有限公司 | Method for configuration and scheduling of Pod between clusters based on Kubernetes |
| CN116132447A (en) * | 2022-12-21 | 2023-05-16 | 天翼云科技有限公司 | Load balancing method and device based on Kubernetes |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112650363A (en) * | 2020-12-11 | 2021-04-13 | 北京四方继保工程技术有限公司 | Distributed telemechanical system based on balanced communication CPU node group and design method |
| CN112650363B (en) * | 2020-12-11 | 2024-05-10 | 北京四方继保工程技术有限公司 | Distributed remote engine system based on balanced communication CPU node group and design method |
| CN113626282A (en) * | 2021-07-16 | 2021-11-09 | 济南浪潮数据技术有限公司 | Cloud computing physical node load monitoring method and device, terminal and storage medium |
| CN113626282B (en) * | 2021-07-16 | 2023-12-22 | 济南浪潮数据技术有限公司 | Cloud computing physical node load monitoring method, device, terminal and storage medium |
| CN113568746A (en) * | 2021-07-27 | 2021-10-29 | 北京达佳互联信息技术有限公司 | Load balancing method and device, electronic equipment and storage medium |
| CN113568746B (en) * | 2021-07-27 | 2024-01-02 | 北京达佳互联信息技术有限公司 | Load balancing method and device, electronic equipment and storage medium |
| CN113656046A (en) * | 2021-08-31 | 2021-11-16 | 北京京东乾石科技有限公司 | Application deployment method and device |
| CN114466019A (en) * | 2022-04-11 | 2022-05-10 | 阿里巴巴（中国）有限公司 | Distributed computing system, load balancing method, device and storage medium |
| CN114466019B (en) * | 2022-04-11 | 2022-09-16 | 阿里巴巴（中国）有限公司 | Distributed computing system, load balancing method, device and storage medium |
| CN114675956A (en) * | 2022-04-14 | 2022-06-28 | 三峡智控科技有限公司 | Method for configuration and scheduling of Pod between clusters based on Kubernetes |
| CN114675956B (en) * | 2022-04-14 | 2022-08-30 | 三峡智控科技有限公司 | Method for configuration and scheduling of Pod between clusters based on Kubernetes |
| CN116132447A (en) * | 2022-12-21 | 2023-05-16 | 天翼云科技有限公司 | Load balancing method and device based on Kubernetes |
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